556results about How to "Easy to capture" patented technology

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

The invention provides a riverbed evolution prediction method based on multi-source data fusion, and belongs to the technical field of hydrological resource monitoring. The method comprises the following steps: acquiring and fusing multi-source data by using different devices, performing data preprocessing to form unified three-dimensional grid data, and then generating a multi-scale feature map based on a designed feature extraction network; based on the designed multi-channel convolutional neural network, learning and extracting features on multiple channels at the same time, and performing hierarchical expression and fusion on different information sources; and finally, based on the designed time sequence regression network, extracting a complex dependency relationship in the time sequence through a double-layer LSTM structure, learning residual error information through an adaptive residual error correction module to improve prediction precision, and obtaining a final prediction result. The method shows relatively high applicability and robustness in a complex river channel environment, and can cope with various river bed form changes caused by natural factors and human activities through multi-source data fusion and an innovative feature extraction and regression method.

The invention discloses a human body behavior recognition method and system based on a space-time fusion network, and relates to the technical field of behavior recognition, and the method comprises the steps: obtaining a human body skeleton sequence, carrying out the joint position coding of the sequence, taking the coded sequence as an input feature, inputting the input feature into the space-time fusion network based on graph convolution and Transform, and outputting a human body behavior recognition result; wherein the space-time fusion network comprises a plurality of groups of graph convolution Transform space fusion modules and multi-scale time fusion modules which are connected in sequence, after input features are input, local structure features and global space features are extracted through the parallel adaptive graph convolution module and Transform module respectively, feature fusion is carried out through the correlation fusion module, and the local structure features and the global space features are fused through the correlation fusion module. And the fused features are input into a multi-scale time fusion module, and are subjected to feature extraction through a multi-scale convolution branch and a Transform branch and then spliced, so that a more accurate human behavior recognition result is output.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention discloses a brain tumor MRI image segmentation model and method based on an improved Swin UNETR network, and belongs to the technical field of image segmentation. The brain tumor MRI image segmentation model is obtained by improving a Swin UNETR as a baseline model and comprises an encoder, a jump connection and a decoder, the decoder comprises a plurality of convolution fusion modules and an attention mechanism module, feature information of different levels is fused in the decoder by using the plurality of convolution fusion modules, and the attention mechanism module is used for identifying the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the convolution fusion modules. And the attention mechanism module is used to focus the feature information obtained by the plurality of convolutions. By adopting the scheme of the invention, the problem that the existing medical image segmentation technology based on deep learning is insufficient in brain tumor MRI image segmentation precision and is difficult to realize accurate segmentation especially in the face of different types of tumors and variability can be effectively solved, the calculation amount is small, and the image segmentation efficiency can be effectively improved.

The invention belongs to the field of artificial intelligence and finance, and relates to a text classification method, which comprises the following steps: preprocessing an original text data set, labeling classification labels to obtain a labeled text data set, inputting a training set into a large language model for keyword extraction to obtain a candidate keyword set, cleaning the candidate keyword set to obtain a classification keyword set, constructing an instruction template based on the classification keyword set, and inputting the training set and the instruction template into a large language model to obtain a prediction classification result; and based on a prediction classification result and a loss function between the classification labels, performing fine tuning on the large language model to obtain a fine tuning model, evaluating that the fine tuning model is qualified to obtain a text classification model, and inputting the to-be-classified text into the text classification model for classification. The invention further provides a text classification device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the tagged text data set can be stored in a block chain. According to the method, the text classification accuracy and stability can be improved.

The invention discloses an optical cable routing anti-intrusion method and system, and the method comprises the following steps: laying a sensor network at key nodes along an optical cable, monitoring the data of a surrounding environment in real time, constructing an environment reference model, and integrating the model to a GIS platform, thereby achieving the risk assessment of different regions. Different monitoring frequencies and sensitivities are set for a high-risk area and a low-risk area according to an evaluation result, an anomaly detection module compares real-time signal data with an environment reference model, high-credibility abnormal signals are screened out through self-adaptive threshold adjustment, and when a physical barrier module detects abnormal external force, the physical barrier module sends out the abnormal external force. Triggering the area and the adjacent area to enter a high-frequency monitoring mode to form a dense monitoring area, positioning an intrusion position through a sensor network and a GIS platform, generating a path prediction model, and dynamically adjusting the frequency, sensitivity and alarm threshold value of the monitoring area based on data analysis and GIS geographic information. The adaptability and the resource configuration efficiency of the system in different environments are improved.

The invention relates to the technical field of ozone concentration prediction, in particular to an ozone concentration prediction method, device, equipment and medium based on a space-time diagram convolutional network model.The method comprises the steps that historical ozone concentration and meteorological data of a target area are obtained, and training and prediction samples are constructed after standardization; a space diagram structure is established, nodes represent monitoring points, and edges represent the monitoring points with correlation exceeding a threshold value; extracting a spatial dependency relationship by adopting GCN and GAT, and generating time sequence data; tCN or RNN is adopted to extract time-dependent features, and spatial-temporal feature comprehensive representation is obtained; training a model by utilizing the training sample and the spatial-temporal characteristics, adopting MSE (mean square error) as a loss function, adjusting parameters by an Adam optimizer, and optimizing hyper-parameters to minimize errors; newest meteorological and historical data are input, future ozone concentration is predicted, GNN extracts spatial features, RNN processes a time sequence, time sequence data can be processed in parallel through convolution operation, and high calculation efficiency is achieved.

The invention discloses a multi-scale feature fusion triple branch network method for multi-organ segmentation, and the method comprises the following steps: extracting local features through a CNN, and extracting global features through Transform; the features extracted by the CNN and the Transfomer are processed by using space attention and channel attention respectively, the processed features are fused, and output features are obtained through a feature processing module; the method comprises the following steps: setting a mask mechanism Smart Mask, improving a fixed mask mode in a Swin Transform, and capturing a long-distance dependency relationship; according to the method, global features in a medical image are learned through parallel Vision Mama, features obtained after CNN and Transform fusion are guided, and a segmented image is obtained. The CNN, Transform and Mamba encoders are combined, so that efficient medical image segmentation is realized. And the introduced parallel Vision Mama layer increases a small number of parameters and enhances the global context capture capability at the same time, so that the performance and efficiency of the model are remarkably improved.

The invention relates to the field of geological disaster prediction, and discloses a geological disaster real-time monitoring and early warning system based on Internet of Things and artificial intelligence, and the system comprises a sensing module which is used for collecting multi-source data of geological disasters in real time through all Internet of Things sensors, and visually displaying potential geological disaster hidden danger signs on the ground surface; the transmission module is used for transmitting various data and guaranteeing the data transmission security by adopting an encryption technology; the processing module is used for sorting and processing the multi-source data and converting the multi-source data into processing data in a standardized data format; and the model module is used for processing data and applying a model fusing a physical principle and a machine learning algorithm. An advanced early warning model is constructed by applying an artificial intelligence technology and fusing a physical principle and a machine learning algorithm, a deep learning optimization unit customizes a strategy for geological disaster images and time sequence data characteristics, and a knowledge graph unit introduces geological domain knowledge to assist modeling.

The invention discloses a multi-class defect detection method and device based on a variational auto-encoder and a denoising network, and the method comprises the steps: obtaining an industrial product surface defect image, carrying out the preprocessing, and constructing a training set and a verification set; the training process of the method is divided into two stages: in the first stage, a preprocessed training image is input into a variational auto-encoder for training, feature distribution of the image is learned, and features of a normal image are captured by reconstructing the image; in the second stage, a denoising network and a feature guiding module are used for conducting secondary reconstruction on image features in the feature space of the variational auto-encoder, and therefore the reconstruction capacity and the multi-class reconstruction capacity of the model are enhanced; in the reasoning stage, multi-scale features of an original image and a reconstructed image are extracted through a feature extractor, feature comparison is carried out, and an abnormal scoring graph is generated. According to the method, normal information is reserved while the reconstruction capability of the variational auto-encoder is enhanced, and defect missing detection caused by reconstruction failure is effectively reduced.

The invention discloses a traditional Chinese medicine recommendation method based on knowledge driving and a residual attention network, and the method comprises the following steps: S1, a data collection and cleaning module: obtaining prescription text data, executing a standardized process for symptoms and traditional Chinese medicine terms in the prescription text data, and dividing a data set after the standardized process is executed into a training set, a verification set and a test set; s2, an entity pre-training module: extracting symptom semantic features by using a Word2vec training model to extract the features of symptom entities in the prescription and obtain overall context semantic representation, constructing a traditional Chinese medicine attribute knowledge graph, and fusing the natural attribute features of the traditional Chinese medicine as external knowledge into a recommendation model; s3, a multi-graph construction and entity feature learning module: according to a co-occurrence relationship between the symptoms and the traditional Chinese medicines, constructing a symptom-traditional Chinese medicine isomerism graph (SHHG), a symptom-symptom isomerism graph (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isomerism graph (HHIG); respectively extracting symptom features, traditional Chinese medicine features and interaction features from a symptom-traditional Chinese medicine isomeric graph (SHHG), a symptom-symptom isocomposition (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isocomposition (HHIG) by using a graph attention neural network, and enhancing entity features by using a residual structure; the feature fusion and traditional Chinese medicine recommendation module is used for fusing symptom features and traditional Chinese medicine features extracted from a symptom-traditional Chinese medicine isomerism graph (SHHG), a symptom-symptom isomerism graph (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isomerism graph (HHIG) to obtain final symptom features and traditional Chinese medicine features, and expressing the symptom features by using MLP to obtain a traditional Chinese medicine recommendation result. And finally recommending a proper traditional Chinese medicine set according to the given symptom set.

The invention relates to the technical field of wireless communication testing, and particularly discloses a Bluetooth headset anti-interference performance testing system which comprises an interference signal generation module, an interference signal dynamic simulation module, a signal stability monitoring and evaluation module and an anti-interference capability identification module. An interference signal comprising a modulation signal, a cross-band noise signal and a dynamic radio frequency signal is generated and injected into a test environment, and a real interference scene is simulated by using dynamic conversion of signal strength and frequency; the monitoring module obtains the intensity and frequency characteristics of the interference signal in real time, and comprehensively evaluates the signal stability based on an intensity abnormal coefficient and a frequency abnormal coefficient; and the anti-interference capability identification module calculates an anti-interference capability coefficient by using a support vector machine in combination with performance data of the Bluetooth earphone under different interference levels, and realizes graded evaluation of earphone performance.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention discloses an optimized dispatching control method and system for transformer area energy storage, and relates to the technical field of energy storage dispatching, and the method comprises the following steps: integrating an intelligent load management system with an energy storage system, thereby monitoring the electrical load of a transformer area in real time, and feeding back the real-time data to the energy storage system; historical load data and electricity price data are collected, a prediction model is trained, future load and electricity price are predicted in real time, and prediction data are generated. And according to a prediction result, a preliminary energy storage system scheduling strategy is formulated, and a target function R is constructed, so that the target function R can dynamically adapt to changing load and electricity price conditions. According to the invention, through tight integration of the intelligent load management system and the energy storage system, real-time monitoring of the power grid load and efficient scheduling of the energy storage system are realized; in other words, the energy storage system can charge or discharge at the best time in combination with accurate load and electricity price prediction and scheduling strategy rules, power grid resources are utilized to the maximum extent, and unnecessary resource waste is avoided.

The invention discloses a sea ice target detection method based on multi-dimensional feature extraction, and the method specifically comprises the following steps: S1, data preprocessing: carrying out the denoising of an original sea ice image through a Gaussian filter, carrying out the multi-scale analysis of the sea ice image through an image pyramid, and obtaining sea ice images under different scales; s2, local feature extraction: extracting texture features of each local region by using a gray-level co-occurrence matrix; s3, performing enhanced texture feature processing on the extracted texture features through a directional filter and an enhancement algorithm to obtain final texture feature data; and S4, performing feature fusion on the obtained multi-dimensional features and the texture features to form a comprehensive feature image, and obtaining a classification result of the sea ice target. According to the sea ice target detection method, through multi-scale analysis, image representation is obtained under different scales by using an image pyramid transformation method, changes and expressions of texture features under different scales are captured, and the stability of sea ice target detection is improved.

The invention discloses a three-phase permanent magnet synchronous motor full-speed-domain driving control system and a control method thereof. The system is composed of a torque limiting module, a full-speed-domain control neural network module, a current control module, a coordinate transformation module-I, a coordinate transformation module-II, a coordinate inverse transformation module, an SVPWM module, a SiC MOSFET driving module, a full-speed-domain position rotating speed observation module, a weak magnetic feedback module and a three-phase permanent magnet synchronous motor. The control method is used for driving the control system. According to the method, accurate tracing of the optimal dq-axis reference current working point is realized based on deep learning, and accurate control of three operation modes of MTPA, field weakening control and MTPV is realized; an improved mobile vision field estimation algorithm is adopted, and the rotor position and the rotor rotating speed are accurately estimated within the full-speed domain range; the SiC MOSFET is used as an inverter switching tube, and a brand new drive circuit is designed to adapt to the operation condition of the electric vehicle.

The invention relates to the technical field of ship lock slope monitoring, in particular to a ship lock slope displacement monitoring and early warning method and system based on hull operation influence. The method comprises the following steps: carrying out ship lock slope multi-source data real-time acquisition on ship lock slope area nodes to obtain real-time ship lock slope multi-source displacement factor data; ship lock slope displacement prediction processing is carried out based on the real-time ship lock slope multi-source displacement factor data, and ship lock slope displacement prediction data is generated; based on the ship lock slope displacement prediction data, time sequence space evolution trend analysis and time sequence space evolution trend analysis of ship lock slope displacement are carried out, and space-time evolution ship lock slope deformation data are generated; and performing ship lock slope displacement monitoring and early warning state evaluation processing based on the space-time evolution ship lock slope deformation data to generate ship lock slope displacement monitoring and early warning state evaluation data. According to the ship lock slope displacement monitoring system and method, accurate monitoring of ship lock slope displacement is achieved by analyzing multiple factors related to the ship lock slope.

The invention relates to the technical field of novel power system optimization operation, in particular to a new energy high-uncertainty scene generation method and device based on a CWGAN-GP model, and the method comprises the steps: correcting an initial generative adversarial network through employing a Wasserstein distance and a gradient penalty term, so as to obtain a final generative adversarial network, and the target convolutional neural network is introduced into the final generative adversarial network to obtain a conditional deep convolutional generative adversarial network of improved deep convolution, so that WGAN-GP is introduced into the conditional deep convolutional generative adversarial network of improved deep convolution to obtain a CWGAN-GP model of improved deep convolution. And a target L-ISODATA clustering algorithm is applied to cluster the generated wind and light scenes so as to generate a high-uncertainty new energy power generation output scene. Therefore, the problems that in the related technology, when the GAN processes high-dimensional data, complex space-time characteristics are difficult to effectively capture, and due to lack of prior information, the training direction is prone to deviation, and the scene generation effect and stability are affected are solved.

The invention discloses a stereo matching method based on context geometric features and distortion parallax optimization, which comprises the following steps of: firstly, shooting a calibrated left picture and a calibrated right picture through a binocular camera, extracting multi-scale context features of the two pictures, and calculating the similarity between feature points according to the feature points of the left feature graph and the right feature graph under different parallaxes; the matching cost is the matching cost, so that the constructed geometric cube contains rich geometric matching information. Meanwhile, context features are copied and expanded into context feature cubes with the same size according to parallax, and rich context information is contained. The geometric cube contains matching conditions of feature points of left and right views with different parallax errors, cost aggregation is guided through context features of the left view, and context feature information, feature point cost matching information under different parallax errors and geometric information of adjacent feature points are preliminarily fused to improve cost matching reliability. And generating a geometric attention weight cube. And filtering the contextual feature cube.

The invention discloses a generative question and answer method and device based on classification guidance and a readable medium, and the method comprises the steps: constructing an original dialogue data set, and training to obtain a trained theme classification model, a trained intention classification model and a trained action recognition model; extracting partially processed dialogue corpora from the original dialogue data set, constructing a classification guide instruction dialogue data set by utilizing the trained subject classification model, the trained intention classification model and the trained action recognition model, and mixing the original dialogue data set and the classification guide instruction dialogue data set to obtain a fine tuning data set; performing fine tuning on the pre-trained generative question and answer model by adopting the fine tuning data set to obtain a fine-tuned generative question and answer model; and obtaining a historical dialogue to be replied, and inputting the historical dialogue to the fine-tuned generative question and answer model to obtain a corresponding output statement. According to the invention, the guidance of the dialogue can be enhanced, and the answer is controllable.

The invention discloses an urban underground space structure health detection method and detection system, and the method comprises the steps: S1, collecting the multi-modal data of an underground space structure through the deployment of diversified sensors; s2, preprocessing the collected data, wherein the preprocessing comprises filtering denoising and feature extraction; s3, performing pattern recognition and health state evaluation on the processed data by using a deep learning model; s4, dynamically adjusting a threshold value of health assessment according to the environmental parameters, and predicting a future health trend; and S5, the evaluation result is displayed through a visual platform, a corresponding health examination report is generated, the health condition of the underground space structure can be monitored in real time by integrating multiple sensors and utilizing a data processing technology, potential problems can be found in time, an early warning threshold value can be dynamically adjusted according to real-time monitoring data through a dynamic threshold value adjusting mechanism, and the early warning efficiency is improved. The problems of false alarm and missing alarm caused by a fixed threshold value are avoided, and the accuracy and reliability of early warning are improved. The method is suitable for various underground space structures.

The invention discloses a water plant ultra-short-term water supply prediction method and system based on time multi-scale characteristics, and relates to the technical field of water resource management and prediction. The method aims at solving the problems that historical water supply data is not fully researched and multiple scales of time are not considered in existing water supply prediction. The method comprises the steps of firstly collecting historical water supply data and related environmental factors of a water plant, preprocessing the data and dividing the data into a training set, a verification set and a test set; selecting historical water supply characteristic factors and related environment characteristic factors from a time multi-scale angle to form a characteristic set; verifying the correlation and potential relationship between the feature set and the water supply amount through correlation analysis, mutual information analysis and a feature histogram method; and then seven deep learning models are constructed based on a mean square error loss function to carry out water supply prediction research, and a Bayesian optimization technology is used to carry out hyper-parameter tuning.

The invention belongs to the technical field of biological feature recognition, and relates to a Transform-based gene data mining method, which comprises the following steps of: loading gene expression data from a file, and preprocessing; inputting the data into a designed model for training and evaluation; the designed model comprises a CNN (Convolutional Neural Network) and an improved Transform model; the improved Transform model comprises an encoder and a classifier; the encoder adopts a multi-head self-attention mechanism, a convolutional self-attention mechanism and a feedforward neural network; processing the data by using the trained model, and storing the fusion attention weight obtained by the coding layer in the data processing process; and finally, averaging the fusion attention weights of all batches, calculating the comprehensive attention weight of each gene at different time points, and identifying the most important gene by calculating and analyzing the attention score of each gene. According to the method, the problems of scarcity and imbalance of gene data can be solved, the risk of overfitting is reduced, and the precision of gene data analysis is improved.

The invention relates to a thermal power plant safety monitoring and early warning management method and system based on artificial intelligence. The method comprises the following steps: acquiring vibration data of equipment of a thermal power plant at each moment within a preset time interval; dividing the vibration data into a plurality of clusters based on a clustering algorithm; generating vibration data features based on the plurality of clusters; inputting the vibration data features into a trained artificial intelligence model to obtain a fault prediction result; and generating a corresponding maintenance strategy based on the fault prediction result, the maintenance strategy being used for indicating to maintain the equipment. According to the method, the equipment vibration data is analyzed and subjected to feature extraction through a clustering algorithm, complex modes and internal structures in the data can be better captured, more accurate information is provided for subsequent model training and fault prediction, and therefore the accuracy and reliability of safety monitoring and early warning are improved.

The invention discloses a wafer detection model training method, a wafer detection method, a wafer detection system, wafer detection equipment and a medium, and the method comprises the steps: obtaining a wafer surface defect image set, carrying out the category marking of the wafer surface defect image set, constructing a wafer defect detection data set, constructing an improved RT-DETR model, and carrying out the training of the improved RT-DETR model according to the wafer defect detection data set. Training the to-be-detected image of the wafer until the training is finished to obtain a wafer detection model, inputting the to-be-detected image of the wafer into a pre-trained wafer detection model, outputting a detection result of the to-be-detected image of the wafer, outputting a defect probability and a mark of the to-be-detected image of the wafer, and obtaining the defect probability of the to-be-detected image of the wafer through an improved RT-DERT model. The detection performance on small particles, slender scratches and the like is effectively improved.

The invention belongs to the technical field of yarn production detection and image processing, and discloses a lightweight yarn defect detection method based on an improved YOLOv8n network, which comprises the following steps: collecting yarn pictures on a production line, preprocessing the collected pictures, inputting the preprocessed pictures into a DGL-YOLO network trained offline, and outputting yarn defect detection pictures with abnormal positions and types. The DGL-YOLO network is based on a YOLOv8n network, a CBS module except the first two CBS modules in the Backbone and a CBS module in the Neck are all replaced by a DMAConv module, and a C2f module in the Backbone and a C2f module in the Neck are all replaced by a C2f-GhostDynamicConv module. The detection method provided by the invention has high confidence coefficient in yarn abnormity identification, and is suitable for being deployed on mobile equipment of a yarn production line to complete a task of real-time yarn detection.

The invention relates to the technical field of federated learning, and discloses a discriminator knowledge distillation personalized federated learning method based on a conditional generative adversarial network. The problems of appearance of massive participants and influence of non-independent identical distribution of data on federal learning data cost and personalized learning efficiency are solved; a discriminator knowledge distillation model fused with the conditional generative adversarial network is put forward to be embedded into a personalized federated learning framework; the differentiated feature representation of the client model is enhanced through the discriminator knowledge distillation model, so that the personalized model effectively recognizes and captures specific modes and features of individuals, the representation capability and the personalized effect of the model are enhanced, the higher model compression rate is achieved, and meanwhile, the user experience is improved. The conditional generative adversarial network fused with the discriminator generates synthetic data specific to each client, and the synthetic data improves the generalization ability of personalized federal learning and the adaptability of the model on the premise of fully ensuring the data privacy.

The invention discloses a change detection method based on semantic prior and fusion space positioning, and relates to the technical field of image processing, and the method comprises the steps: firstly, carrying out the pre-training of a BPNet network on a supervised remote sensing extraction task, and fully utilizing the prior knowledge in the remote sensing extraction task; and then fine tuning is performed on the pre-trained BPNet network, a twinning network structure sharing weight is adopted to expand an encoder and decoder structure, a space consistency attention module is introduced, and feature information extraction and fusion are performed on the dual-tense image by combining a self-attention mechanism and a large convolution kernel decomposition technology so as to obtain a dual-tense image. Therefore, remote sensing image target change information is obtained. Therefore, by adopting the change detection method based on semantic prior and fusion space positioning, through the space consistency attention module, the depth encoder can be helped to expand an effective receptive field, the modeling capability of a long-distance dependency relationship is enhanced, and meanwhile, double-tense change detail information can be effectively captured, and a more accurate feature map can be obtained.

The invention discloses a short-time traffic flow prediction method and system based on a multi-feature map, and relates to the technical field of traffic flow prediction, and the method comprises the steps: respectively constructing an occupancy rate feature map, an average speed feature map and a traffic flow feature map through three kinds of data, including traffic flow, occupancy rate and average speed data, collected by a road sensor at the same time; node feature information of three feature maps is aggregated by using a GCN, supervised learning training of occupancy features and average speed features is realized by using an improved Transform encoder, and feature interaction fusion of obtained feature vectors of occupancy and average speed is realized in a multi-feature fusion traffic flow prediction model. The occupancy and average speed features are utilized to assist a multi-feature fusion traffic flow prediction model to better capture traffic flow features, and more accurate traffic flow prediction is provided.